Education, fascism, memory, and the crisis of democracy in the 21st Century: Rethinking 9/11
Bibliographic record
Abstract
This article examines the intersection of memory, militarism, and the rise of fascism in the United States in the aftermath of 9/11. It explores how the post-9/11 era, initially marked by national unity and solidarity, quickly devolved into militarism, xenophobia, and the erosion of civil liberties. The manipulation of collective memory and a regressive form of education have played a central role in the rise of far-right authoritarianism, with fear and historical amnesia fueling fascist politics. The commodification of loss and the disavowal of collective responsibility have undermined democracy, contributing to the current crisis of authoritarianism in America. Central to the fight against this authoritarian drift is the crucial notion of making education central to politics and the production of what might be called a radical democratic imaginary, which plays a vital role in shaping critically informed citizens. The article emphasizes that reclaiming educational spaces—both formal and cultural—as sites of critical inquiry and resistance is essential for resisting fascism and reimagining a democratic future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".